Overview

Brought to you by YData

Dataset statistics

Number of variables53
Number of observations6014
Missing cells0
Missing cells (%)0.0%
Duplicate rows12
Duplicate rows (%)0.2%
Total size in memory2.4 MiB
Average record size in memory424.0 B

Variable types

Numeric6
Categorical47

Alerts

Dataset has 12 (0.2%) duplicate rowsDuplicates
engine is highly overall correlated with fuel_Diesel and 5 other fieldsHigh correlation
fuel_Diesel is highly overall correlated with engine and 1 other fieldsHigh correlation
fuel_Petrol is highly overall correlated with engine and 1 other fieldsHigh correlation
km_driven is highly overall correlated with yearHigh correlation
max_power is highly overall correlated with engine and 3 other fieldsHigh correlation
mileage is highly overall correlated with seats_5High correlation
name_Mahindra is highly overall correlated with engine and 1 other fieldsHigh correlation
owner_Test Drive Car is highly overall correlated with selling_priceHigh correlation
seats_4 is highly overall correlated with max_powerHigh correlation
seats_5 is highly overall correlated with engine and 3 other fieldsHigh correlation
seats_7 is highly overall correlated with engine and 1 other fieldsHigh correlation
selling_price is highly overall correlated with max_power and 2 other fieldsHigh correlation
transmission_Manual is highly overall correlated with max_powerHigh correlation
year is highly overall correlated with km_driven and 1 other fieldsHigh correlation
name_Audi is highly imbalanced (95.3%) Imbalance
name_BMW is highly imbalanced (94.0%) Imbalance
name_Chevrolet is highly imbalanced (79.6%) Imbalance
name_Daewoo is highly imbalanced (99.4%) Imbalance
name_Datsun is highly imbalanced (92.9%) Imbalance
name_Fiat is highly imbalanced (94.4%) Imbalance
name_Force is highly imbalanced (99.2%) Imbalance
name_Ford is highly imbalanced (70.5%) Imbalance
name_Honda is highly imbalanced (70.2%) Imbalance
name_Isuzu is highly imbalanced (99.2%) Imbalance
name_Jaguar is highly imbalanced (98.5%) Imbalance
name_Jeep is highly imbalanced (96.8%) Imbalance
name_Kia is highly imbalanced (99.4%) Imbalance
name_Land is highly imbalanced (99.4%) Imbalance
name_Lexus is highly imbalanced (99.8%) Imbalance
name_MG is highly imbalanced (99.4%) Imbalance
name_Mahindra is highly imbalanced (51.7%) Imbalance
name_Mercedes-Benz is highly imbalanced (93.8%) Imbalance
name_Mitsubishi is highly imbalanced (98.5%) Imbalance
name_Nissan is highly imbalanced (91.6%) Imbalance
name_Peugeot is highly imbalanced (99.8%) Imbalance
name_Renault is highly imbalanced (80.7%) Imbalance
name_Skoda is highly imbalanced (91.8%) Imbalance
name_Tata is highly imbalanced (56.2%) Imbalance
name_Toyota is highly imbalanced (70.4%) Imbalance
name_Volkswagen is highly imbalanced (82.6%) Imbalance
name_Volvo is highly imbalanced (98.5%) Imbalance
fuel_LPG is highly imbalanced (95.0%) Imbalance
seller_type_Individual is highly imbalanced (52.1%) Imbalance
seller_type_Trustmark Dealer is highly imbalanced (96.1%) Imbalance
transmission_Manual is highly imbalanced (58.2%) Imbalance
owner_Fourth & Above Owner is highly imbalanced (84.0%) Imbalance
owner_Test Drive Car is highly imbalanced (99.2%) Imbalance
owner_Third Owner is highly imbalanced (61.2%) Imbalance
seats_4 is highly imbalanced (88.1%) Imbalance
seats_6 is highly imbalanced (93.2%) Imbalance
seats_8 is highly imbalanced (79.3%) Imbalance
seats_9 is highly imbalanced (91.0%) Imbalance
seats_10 is highly imbalanced (97.1%) Imbalance
seats_14 is highly imbalanced (99.8%) Imbalance

Reproduction

Analysis started2024-11-27 19:13:11.440237
Analysis finished2024-11-27 19:13:39.433027
Duration27.99 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

year
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.4475
Minimum1983
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.1 KiB
2024-11-27T19:13:39.606958image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1983
5-th percentile2006
Q12011
median2014
Q32017
95-th percentile2019
Maximum2020
Range37
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0799204
Coefficient of variation (CV)0.0020263357
Kurtosis1.6891091
Mean2013.4475
Median Absolute Deviation (MAD)3
Skewness-1.0198946
Sum12108873
Variance16.64575
MonotonicityNot monotonic
2024-11-27T19:13:39.897913image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2017 696
11.6%
2016 615
10.2%
2015 585
9.7%
2018 536
8.9%
2012 524
8.7%
2014 503
8.4%
2013 488
8.1%
2011 469
7.8%
2010 329
 
5.5%
2019 306
 
5.1%
Other values (19) 963
16.0%
ValueCountFrequency (%)
1983 1
 
< 0.1%
1991 1
 
< 0.1%
1994 3
 
< 0.1%
1995 1
 
< 0.1%
1996 3
 
< 0.1%
1997 10
0.2%
1998 9
0.1%
1999 12
0.2%
2000 19
0.3%
2001 7
 
0.1%
ValueCountFrequency (%)
2020 59
 
1.0%
2019 306
5.1%
2018 536
8.9%
2017 696
11.6%
2016 615
10.2%
2015 585
9.7%
2014 503
8.4%
2013 488
8.1%
2012 524
8.7%
2011 469
7.8%

selling_price
Real number (ℝ)

High correlation 

Distinct637
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521982.03
Minimum29999
Maximum10000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.1 KiB
2024-11-27T19:13:40.270175image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum29999
5-th percentile100000
Q1250000
median409999
Q3640000
95-th percentile1200000
Maximum10000000
Range9970001
Interquartile range (IQR)390000

Descriptive statistics

Standard deviation533842.62
Coefficient of variation (CV)1.0227222
Kurtosis52.713853
Mean521982.03
Median Absolute Deviation (MAD)190001
Skewness5.6236928
Sum3.1391999 × 109
Variance2.8498794 × 1011
MonotonicityNot monotonic
2024-11-27T19:13:40.677099image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300000 188
 
3.1%
350000 173
 
2.9%
400000 147
 
2.4%
250000 144
 
2.4%
550000 142
 
2.4%
600000 142
 
2.4%
500000 138
 
2.3%
450000 137
 
2.3%
200000 133
 
2.2%
650000 125
 
2.1%
Other values (627) 4545
75.6%
ValueCountFrequency (%)
29999 1
 
< 0.1%
30000 2
 
< 0.1%
31504 1
 
< 0.1%
35000 3
 
< 0.1%
39000 1
 
< 0.1%
40000 12
0.2%
42000 2
 
< 0.1%
45000 16
0.3%
45957 1
 
< 0.1%
50000 15
0.2%
ValueCountFrequency (%)
10000000 1
 
< 0.1%
7200000 1
 
< 0.1%
6523000 1
 
< 0.1%
6223000 1
 
< 0.1%
6000000 3
< 0.1%
5923000 1
 
< 0.1%
5850000 1
 
< 0.1%
5830000 1
 
< 0.1%
5800000 2
< 0.1%
5500000 3
< 0.1%

km_driven
Real number (ℝ)

High correlation 

Distinct827
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73764.15
Minimum1
Maximum2360457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.1 KiB
2024-11-27T19:13:40.987405image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11325
Q139000
median70000
Q3100000
95-th percentile155000
Maximum2360457
Range2360456
Interquartile range (IQR)61000

Descriptive statistics

Standard deviation59610.747
Coefficient of variation (CV)0.80812627
Kurtosis416.83816
Mean73764.15
Median Absolute Deviation (MAD)30000
Skewness12.58954
Sum4.436176 × 108
Variance3.5534411 × 109
MonotonicityNot monotonic
2024-11-27T19:13:41.322969image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000 446
 
7.4%
70000 373
 
6.2%
80000 371
 
6.2%
60000 342
 
5.7%
50000 313
 
5.2%
100000 290
 
4.8%
90000 280
 
4.7%
40000 238
 
4.0%
110000 229
 
3.8%
30000 192
 
3.2%
Other values (817) 2940
48.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
1000 5
0.1%
1300 1
 
< 0.1%
1303 1
 
< 0.1%
1500 2
 
< 0.1%
1600 1
 
< 0.1%
1620 1
 
< 0.1%
2000 6
0.1%
2118 1
 
< 0.1%
2136 1
 
< 0.1%
ValueCountFrequency (%)
2360457 1
< 0.1%
1500000 1
< 0.1%
577414 1
< 0.1%
500000 2
< 0.1%
475000 1
< 0.1%
440000 1
< 0.1%
426000 1
< 0.1%
380000 1
< 0.1%
376412 1
< 0.1%
370000 1
< 0.1%

mileage
Real number (ℝ)

High correlation 

Distinct375
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.471521
Minimum0
Maximum42
Zeros14
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size47.1 KiB
2024-11-27T19:13:41.637618image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.9585
Q117
median19.3
Q322.32
95-th percentile25.83
Maximum42
Range42
Interquartile range (IQR)5.32

Descriptive statistics

Standard deviation3.9850481
Coefficient of variation (CV)0.20466034
Kurtosis0.83456126
Mean19.471521
Median Absolute Deviation (MAD)2.7
Skewness-0.17579993
Sum117101.73
Variance15.880608
MonotonicityNot monotonic
2024-11-27T19:13:41.946583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.3 247
 
4.1%
18.9 184
 
3.1%
19.7 146
 
2.4%
18.6 130
 
2.2%
21.1 124
 
2.1%
17 108
 
1.8%
15.96 98
 
1.6%
17.8 90
 
1.5%
16.1 86
 
1.4%
15.1 79
 
1.3%
Other values (365) 4722
78.5%
ValueCountFrequency (%)
0 14
0.2%
9 4
 
0.1%
9.5 1
 
< 0.1%
10 2
 
< 0.1%
10.1 2
 
< 0.1%
10.5 14
0.2%
10.71 1
 
< 0.1%
10.75 1
 
< 0.1%
10.8 1
 
< 0.1%
10.9 4
 
0.1%
ValueCountFrequency (%)
42 1
 
< 0.1%
33.44 2
 
< 0.1%
33 1
 
< 0.1%
32.52 1
 
< 0.1%
30.46 2
 
< 0.1%
28.4 74
1.2%
28.09 29
 
0.5%
27.62 5
 
0.1%
27.4 4
 
0.1%
27.39 21
 
0.3%

engine
Real number (ℝ)

High correlation 

Distinct120
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1425.7027
Minimum624
Maximum3604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.1 KiB
2024-11-27T19:13:42.263069image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum624
5-th percentile796
Q11197
median1248
Q31498
95-th percentile2499
Maximum3604
Range2980
Interquartile range (IQR)301

Descriptive statistics

Standard deviation484.72854
Coefficient of variation (CV)0.33999272
Kurtosis1.1330892
Mean1425.7027
Median Absolute Deviation (MAD)213
Skewness1.2666541
Sum8574176
Variance234961.75
MonotonicityNot monotonic
2024-11-27T19:13:42.658897image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1248 985
16.4%
1197 604
 
10.0%
796 360
 
6.0%
998 343
 
5.7%
1498 292
 
4.9%
2179 290
 
4.8%
1396 234
 
3.9%
1199 167
 
2.8%
2523 160
 
2.7%
1461 148
 
2.5%
Other values (110) 2431
40.4%
ValueCountFrequency (%)
624 16
 
0.3%
793 5
 
0.1%
796 360
6.0%
799 61
 
1.0%
814 92
 
1.5%
909 2
 
< 0.1%
936 29
 
0.5%
993 24
 
0.4%
995 40
 
0.7%
998 343
5.7%
ValueCountFrequency (%)
3604 1
 
< 0.1%
3498 1
 
< 0.1%
3198 2
 
< 0.1%
2999 2
 
< 0.1%
2997 2
 
< 0.1%
2993 12
0.2%
2987 8
 
0.1%
2982 23
0.4%
2967 8
 
0.1%
2956 14
0.2%

max_power
Real number (ℝ)

High correlation 

Distinct313
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.686531
Minimum0
Maximum400
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size47.1 KiB
2024-11-27T19:13:43.194502image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47.3
Q168
median81.86
Q399
95-th percentile147.835
Maximum400
Range400
Interquartile range (IQR)31

Descriptive statistics

Standard deviation31.553076
Coefficient of variation (CV)0.35983948
Kurtosis6.0133389
Mean87.686531
Median Absolute Deviation (MAD)14.81
Skewness1.792426
Sum527346.8
Variance995.59659
MonotonicityNot monotonic
2024-11-27T19:13:43.766521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 282
 
4.7%
82 275
 
4.6%
88.5 168
 
2.8%
67 138
 
2.3%
46.3 136
 
2.3%
81.8 120
 
2.0%
62.1 119
 
2.0%
67.1 119
 
2.0%
67.04 117
 
1.9%
70 113
 
1.9%
Other values (303) 4427
73.6%
ValueCountFrequency (%)
0 3
 
< 0.1%
32.8 2
 
< 0.1%
34.2 18
 
0.3%
35 14
 
0.2%
35.5 2
 
< 0.1%
37 71
1.2%
37.48 8
 
0.1%
37.5 6
 
0.1%
38 1
 
< 0.1%
38.4 2
 
< 0.1%
ValueCountFrequency (%)
400 1
 
< 0.1%
282 1
 
< 0.1%
280 1
 
< 0.1%
272 1
 
< 0.1%
270.9 3
< 0.1%
265 1
 
< 0.1%
261.4 4
0.1%
258 2
< 0.1%
254.8 3
< 0.1%
254.79 1
 
< 0.1%

name_Audi
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5983 
1.0
 
31

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5983
99.5%
1.0 31
 
0.5%

Length

2024-11-27T19:13:44.271120image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:44.685216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5983
99.5%
1.0 31
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 11997
66.5%
. 6014
33.3%
1 31
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11997
66.5%
. 6014
33.3%
1 31
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11997
66.5%
. 6014
33.3%
1 31
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11997
66.5%
. 6014
33.3%
1 31
 
0.2%

name_BMW
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5972 
1.0
 
42

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5972
99.3%
1.0 42
 
0.7%

Length

2024-11-27T19:13:45.162020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:45.519310image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5972
99.3%
1.0 42
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 11986
66.4%
. 6014
33.3%
1 42
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11986
66.4%
. 6014
33.3%
1 42
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11986
66.4%
. 6014
33.3%
1 42
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11986
66.4%
. 6014
33.3%
1 42
 
0.2%

name_Chevrolet
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5822 
1.0
 
192

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5822
96.8%
1.0 192
 
3.2%

Length

2024-11-27T19:13:45.998167image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:46.443068image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5822
96.8%
1.0 192
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 11836
65.6%
. 6014
33.3%
1 192
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11836
65.6%
. 6014
33.3%
1 192
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11836
65.6%
. 6014
33.3%
1 192
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11836
65.6%
. 6014
33.3%
1 192
 
1.1%

name_Daewoo
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
6011 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6011
> 99.9%
1.0 3
 
< 0.1%

Length

2024-11-27T19:13:46.797130image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:47.021000image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6011
> 99.9%
1.0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 12025
66.7%
. 6014
33.3%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12025
66.7%
. 6014
33.3%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12025
66.7%
. 6014
33.3%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12025
66.7%
. 6014
33.3%
1 3
 
< 0.1%

name_Datsun
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5963 
1.0
 
51

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5963
99.2%
1.0 51
 
0.8%

Length

2024-11-27T19:13:47.250689image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:47.476743image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5963
99.2%
1.0 51
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 11977
66.4%
. 6014
33.3%
1 51
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11977
66.4%
. 6014
33.3%
1 51
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11977
66.4%
. 6014
33.3%
1 51
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11977
66.4%
. 6014
33.3%
1 51
 
0.3%

name_Fiat
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5975 
1.0
 
39

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5975
99.4%
1.0 39
 
0.6%

Length

2024-11-27T19:13:47.725949image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:47.938464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5975
99.4%
1.0 39
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 11989
66.5%
. 6014
33.3%
1 39
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11989
66.5%
. 6014
33.3%
1 39
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11989
66.5%
. 6014
33.3%
1 39
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11989
66.5%
. 6014
33.3%
1 39
 
0.2%

name_Force
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
6010 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6010
99.9%
1.0 4
 
0.1%

Length

2024-11-27T19:13:48.165917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:48.373836image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6010
99.9%
1.0 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 12024
66.6%
. 6014
33.3%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12024
66.6%
. 6014
33.3%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12024
66.6%
. 6014
33.3%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12024
66.6%
. 6014
33.3%
1 4
 
< 0.1%

name_Ford
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5701 
1.0
 
313

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5701
94.8%
1.0 313
 
5.2%

Length

2024-11-27T19:13:48.614757image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:48.823216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5701
94.8%
1.0 313
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 11715
64.9%
. 6014
33.3%
1 313
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11715
64.9%
. 6014
33.3%
1 313
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11715
64.9%
. 6014
33.3%
1 313
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11715
64.9%
. 6014
33.3%
1 313
 
1.7%

name_Honda
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5697 
1.0
 
317

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5697
94.7%
1.0 317
 
5.3%

Length

2024-11-27T19:13:49.051318image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:49.271910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5697
94.7%
1.0 317
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 11711
64.9%
. 6014
33.3%
1 317
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11711
64.9%
. 6014
33.3%
1 317
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11711
64.9%
. 6014
33.3%
1 317
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11711
64.9%
. 6014
33.3%
1 317
 
1.8%

name_Hyundai
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
4931 
1.0
1083 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 4931
82.0%
1.0 1083
 
18.0%

Length

2024-11-27T19:13:49.500210image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:49.727205image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4931
82.0%
1.0 1083
 
18.0%

Most occurring characters

ValueCountFrequency (%)
0 10945
60.7%
. 6014
33.3%
1 1083
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10945
60.7%
. 6014
33.3%
1 1083
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10945
60.7%
. 6014
33.3%
1 1083
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10945
60.7%
. 6014
33.3%
1 1083
 
6.0%

name_Isuzu
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
6010 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6010
99.9%
1.0 4
 
0.1%

Length

2024-11-27T19:13:49.962003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:50.180554image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6010
99.9%
1.0 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 12024
66.6%
. 6014
33.3%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12024
66.6%
. 6014
33.3%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12024
66.6%
. 6014
33.3%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12024
66.6%
. 6014
33.3%
1 4
 
< 0.1%

name_Jaguar
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
6006 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6006
99.9%
1.0 8
 
0.1%

Length

2024-11-27T19:13:50.416150image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:50.653186image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6006
99.9%
1.0 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 12020
66.6%
. 6014
33.3%
1 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12020
66.6%
. 6014
33.3%
1 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12020
66.6%
. 6014
33.3%
1 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12020
66.6%
. 6014
33.3%
1 8
 
< 0.1%

name_Jeep
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5994 
1.0
 
20

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5994
99.7%
1.0 20
 
0.3%

Length

2024-11-27T19:13:50.877989image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:51.096623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5994
99.7%
1.0 20
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 12008
66.6%
. 6014
33.3%
1 20
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12008
66.6%
. 6014
33.3%
1 20
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12008
66.6%
. 6014
33.3%
1 20
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12008
66.6%
. 6014
33.3%
1 20
 
0.1%

name_Kia
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
6011 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6011
> 99.9%
1.0 3
 
< 0.1%

Length

2024-11-27T19:13:51.321787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:51.544239image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6011
> 99.9%
1.0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 12025
66.7%
. 6014
33.3%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12025
66.7%
. 6014
33.3%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12025
66.7%
. 6014
33.3%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12025
66.7%
. 6014
33.3%
1 3
 
< 0.1%

name_Land
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
6011 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6011
> 99.9%
1.0 3
 
< 0.1%

Length

2024-11-27T19:13:51.777737image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:51.988195image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6011
> 99.9%
1.0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 12025
66.7%
. 6014
33.3%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12025
66.7%
. 6014
33.3%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12025
66.7%
. 6014
33.3%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12025
66.7%
. 6014
33.3%
1 3
 
< 0.1%

name_Lexus
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
6013 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6013
> 99.9%
1.0 1
 
< 0.1%

Length

2024-11-27T19:13:52.232464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:52.451591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6013
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 12027
66.7%
. 6014
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12027
66.7%
. 6014
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12027
66.7%
. 6014
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12027
66.7%
. 6014
33.3%
1 1
 
< 0.1%

name_MG
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
6011 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6011
> 99.9%
1.0 3
 
< 0.1%

Length

2024-11-27T19:13:52.671688image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:52.929183image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6011
> 99.9%
1.0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 12025
66.7%
. 6014
33.3%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12025
66.7%
. 6014
33.3%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12025
66.7%
. 6014
33.3%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12025
66.7%
. 6014
33.3%
1 3
 
< 0.1%

name_Mahindra
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5385 
1.0
629 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5385
89.5%
1.0 629
 
10.5%

Length

2024-11-27T19:13:53.163955image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:53.376586image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5385
89.5%
1.0 629
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 11399
63.2%
. 6014
33.3%
1 629
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11399
63.2%
. 6014
33.3%
1 629
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11399
63.2%
. 6014
33.3%
1 629
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11399
63.2%
. 6014
33.3%
1 629
 
3.5%

name_Maruti
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
4128 
1.0
1886 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4128
68.6%
1.0 1886
31.4%

Length

2024-11-27T19:13:53.631165image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:53.871007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4128
68.6%
1.0 1886
31.4%

Most occurring characters

ValueCountFrequency (%)
0 10142
56.2%
. 6014
33.3%
1 1886
 
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10142
56.2%
. 6014
33.3%
1 1886
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10142
56.2%
. 6014
33.3%
1 1886
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10142
56.2%
. 6014
33.3%
1 1886
 
10.5%

name_Mercedes-Benz
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5970 
1.0
 
44

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5970
99.3%
1.0 44
 
0.7%

Length

2024-11-27T19:13:54.103674image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:54.313281image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5970
99.3%
1.0 44
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 11984
66.4%
. 6014
33.3%
1 44
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11984
66.4%
. 6014
33.3%
1 44
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11984
66.4%
. 6014
33.3%
1 44
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11984
66.4%
. 6014
33.3%
1 44
 
0.2%

name_Mitsubishi
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
6006 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6006
99.9%
1.0 8
 
0.1%

Length

2024-11-27T19:13:54.546795image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:54.757286image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6006
99.9%
1.0 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 12020
66.6%
. 6014
33.3%
1 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12020
66.6%
. 6014
33.3%
1 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12020
66.6%
. 6014
33.3%
1 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12020
66.6%
. 6014
33.3%
1 8
 
< 0.1%

name_Nissan
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5951 
1.0
 
63

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5951
99.0%
1.0 63
 
1.0%

Length

2024-11-27T19:13:54.995210image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:55.215272image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5951
99.0%
1.0 63
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 11965
66.3%
. 6014
33.3%
1 63
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11965
66.3%
. 6014
33.3%
1 63
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11965
66.3%
. 6014
33.3%
1 63
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11965
66.3%
. 6014
33.3%
1 63
 
0.3%

name_Peugeot
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
6013 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6013
> 99.9%
1.0 1
 
< 0.1%

Length

2024-11-27T19:13:55.456972image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:55.690488image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6013
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 12027
66.7%
. 6014
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12027
66.7%
. 6014
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12027
66.7%
. 6014
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12027
66.7%
. 6014
33.3%
1 1
 
< 0.1%

name_Renault
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5835 
1.0
 
179

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5835
97.0%
1.0 179
 
3.0%

Length

2024-11-27T19:13:55.942249image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:56.163326image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5835
97.0%
1.0 179
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 11849
65.7%
. 6014
33.3%
1 179
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11849
65.7%
. 6014
33.3%
1 179
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11849
65.7%
. 6014
33.3%
1 179
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11849
65.7%
. 6014
33.3%
1 179
 
1.0%

name_Skoda
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5953 
1.0
 
61

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5953
99.0%
1.0 61
 
1.0%

Length

2024-11-27T19:13:56.396518image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:56.618776image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5953
99.0%
1.0 61
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 11967
66.3%
. 6014
33.3%
1 61
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11967
66.3%
. 6014
33.3%
1 61
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11967
66.3%
. 6014
33.3%
1 61
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11967
66.3%
. 6014
33.3%
1 61
 
0.3%

name_Tata
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5470 
1.0
 
544

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5470
91.0%
1.0 544
 
9.0%

Length

2024-11-27T19:13:56.960870image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:57.304418image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5470
91.0%
1.0 544
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 11484
63.7%
. 6014
33.3%
1 544
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11484
63.7%
. 6014
33.3%
1 544
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11484
63.7%
. 6014
33.3%
1 544
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11484
63.7%
. 6014
33.3%
1 544
 
3.0%

name_Toyota
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5700 
1.0
 
314

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5700
94.8%
1.0 314
 
5.2%

Length

2024-11-27T19:13:57.706829image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:58.107131image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5700
94.8%
1.0 314
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 11714
64.9%
. 6014
33.3%
1 314
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11714
64.9%
. 6014
33.3%
1 314
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11714
64.9%
. 6014
33.3%
1 314
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11714
64.9%
. 6014
33.3%
1 314
 
1.7%

name_Volkswagen
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5857 
1.0
 
157

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5857
97.4%
1.0 157
 
2.6%

Length

2024-11-27T19:13:58.556760image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:58.928180image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5857
97.4%
1.0 157
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 11871
65.8%
. 6014
33.3%
1 157
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11871
65.8%
. 6014
33.3%
1 157
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11871
65.8%
. 6014
33.3%
1 157
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11871
65.8%
. 6014
33.3%
1 157
 
0.9%

name_Volvo
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
6006 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6006
99.9%
1.0 8
 
0.1%

Length

2024-11-27T19:13:59.393020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:13:59.794099image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6006
99.9%
1.0 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 12020
66.6%
. 6014
33.3%
1 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12020
66.6%
. 6014
33.3%
1 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12020
66.6%
. 6014
33.3%
1 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12020
66.6%
. 6014
33.3%
1 8
 
< 0.1%

fuel_Diesel
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
1.0
3269 
0.0
2745 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 3269
54.4%
0.0 2745
45.6%

Length

2024-11-27T19:14:00.294459image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:14:00.738756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3269
54.4%
0.0 2745
45.6%

Most occurring characters

ValueCountFrequency (%)
0 8759
48.5%
. 6014
33.3%
1 3269
 
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8759
48.5%
. 6014
33.3%
1 3269
 
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8759
48.5%
. 6014
33.3%
1 3269
 
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8759
48.5%
. 6014
33.3%
1 3269
 
18.1%

fuel_LPG
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5980 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5980
99.4%
1.0 34
 
0.6%

Length

2024-11-27T19:14:01.049837image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:14:01.268620image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5980
99.4%
1.0 34
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 11994
66.5%
. 6014
33.3%
1 34
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11994
66.5%
. 6014
33.3%
1 34
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11994
66.5%
. 6014
33.3%
1 34
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11994
66.5%
. 6014
33.3%
1 34
 
0.2%

fuel_Petrol
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
3354 
1.0
2660 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 3354
55.8%
1.0 2660
44.2%

Length

2024-11-27T19:14:01.496289image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:14:01.720152image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 3354
55.8%
1.0 2660
44.2%

Most occurring characters

ValueCountFrequency (%)
0 9368
51.9%
. 6014
33.3%
1 2660
 
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9368
51.9%
. 6014
33.3%
1 2660
 
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9368
51.9%
. 6014
33.3%
1 2660
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9368
51.9%
. 6014
33.3%
1 2660
 
14.7%

seller_type_Individual
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
1.0
5394 
0.0
620 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5394
89.7%
0.0 620
 
10.3%

Length

2024-11-27T19:14:01.958831image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:14:02.204163image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5394
89.7%
0.0 620
 
10.3%

Most occurring characters

ValueCountFrequency (%)
0 6634
36.8%
. 6014
33.3%
1 5394
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6634
36.8%
. 6014
33.3%
1 5394
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6634
36.8%
. 6014
33.3%
1 5394
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6634
36.8%
. 6014
33.3%
1 5394
29.9%

seller_type_Trustmark Dealer
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5989 
1.0
 
25

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5989
99.6%
1.0 25
 
0.4%

Length

2024-11-27T19:14:02.431932image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:14:02.645139image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5989
99.6%
1.0 25
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 12003
66.5%
. 6014
33.3%
1 25
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12003
66.5%
. 6014
33.3%
1 25
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12003
66.5%
. 6014
33.3%
1 25
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12003
66.5%
. 6014
33.3%
1 25
 
0.1%

transmission_Manual
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
1.0
5505 
0.0
 
509

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5505
91.5%
0.0 509
 
8.5%

Length

2024-11-27T19:14:02.875482image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:14:03.095961image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5505
91.5%
0.0 509
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 6523
36.2%
. 6014
33.3%
1 5505
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6523
36.2%
. 6014
33.3%
1 5505
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6523
36.2%
. 6014
33.3%
1 5505
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6523
36.2%
. 6014
33.3%
1 5505
30.5%

owner_Fourth & Above Owner
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5873 
1.0
 
141

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5873
97.7%
1.0 141
 
2.3%

Length

2024-11-27T19:14:03.368588image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:14:03.581992image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5873
97.7%
1.0 141
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 11887
65.9%
. 6014
33.3%
1 141
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11887
65.9%
. 6014
33.3%
1 141
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11887
65.9%
. 6014
33.3%
1 141
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11887
65.9%
. 6014
33.3%
1 141
 
0.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
4323 
1.0
1691 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4323
71.9%
1.0 1691
 
28.1%

Length

2024-11-27T19:14:03.813537image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:14:04.030712image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4323
71.9%
1.0 1691
 
28.1%

Most occurring characters

ValueCountFrequency (%)
0 10337
57.3%
. 6014
33.3%
1 1691
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 10337
57.3%
. 6014
33.3%
1 1691
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 10337
57.3%
. 6014
33.3%
1 1691
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 10337
57.3%
. 6014
33.3%
1 1691
 
9.4%

owner_Test Drive Car
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
6010 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6010
99.9%
1.0 4
 
0.1%

Length

2024-11-27T19:14:04.266310image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:14:04.485756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6010
99.9%
1.0 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 12024
66.6%
. 6014
33.3%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12024
66.6%
. 6014
33.3%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12024
66.6%
. 6014
33.3%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12024
66.6%
. 6014
33.3%
1 4
 
< 0.1%

owner_Third Owner
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5557 
1.0
 
457

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5557
92.4%
1.0 457
 
7.6%

Length

2024-11-27T19:14:04.713361image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:14:04.929126image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5557
92.4%
1.0 457
 
7.6%

Most occurring characters

ValueCountFrequency (%)
0 11571
64.1%
. 6014
33.3%
1 457
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11571
64.1%
. 6014
33.3%
1 457
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11571
64.1%
. 6014
33.3%
1 457
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11571
64.1%
. 6014
33.3%
1 457
 
2.5%

seats_4
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5917 
1.0
 
97

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5917
98.4%
1.0 97
 
1.6%

Length

2024-11-27T19:14:05.916364image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:14:06.122363image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5917
98.4%
1.0 97
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 11931
66.1%
. 6014
33.3%
1 97
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11931
66.1%
. 6014
33.3%
1 97
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11931
66.1%
. 6014
33.3%
1 97
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11931
66.1%
. 6014
33.3%
1 97
 
0.5%

seats_5
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
1.0
4762 
0.0
1252 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 4762
79.2%
0.0 1252
 
20.8%

Length

2024-11-27T19:14:06.366022image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:14:06.605492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4762
79.2%
0.0 1252
 
20.8%

Most occurring characters

ValueCountFrequency (%)
0 7266
40.3%
. 6014
33.3%
1 4762
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 7266
40.3%
. 6014
33.3%
1 4762
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 7266
40.3%
. 6014
33.3%
1 4762
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 7266
40.3%
. 6014
33.3%
1 4762
26.4%

seats_6
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5965 
1.0
 
49

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5965
99.2%
1.0 49
 
0.8%

Length

2024-11-27T19:14:06.835151image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:14:07.053666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5965
99.2%
1.0 49
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 11979
66.4%
. 6014
33.3%
1 49
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11979
66.4%
. 6014
33.3%
1 49
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11979
66.4%
. 6014
33.3%
1 49
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11979
66.4%
. 6014
33.3%
1 49
 
0.3%

seats_7
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5194 
1.0
820 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5194
86.4%
1.0 820
 
13.6%

Length

2024-11-27T19:14:07.290606image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:14:07.513924image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5194
86.4%
1.0 820
 
13.6%

Most occurring characters

ValueCountFrequency (%)
0 11208
62.1%
. 6014
33.3%
1 820
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11208
62.1%
. 6014
33.3%
1 820
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11208
62.1%
. 6014
33.3%
1 820
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11208
62.1%
. 6014
33.3%
1 820
 
4.5%

seats_8
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5818 
1.0
 
196

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5818
96.7%
1.0 196
 
3.3%

Length

2024-11-27T19:14:07.748488image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:14:07.958225image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5818
96.7%
1.0 196
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 11832
65.6%
. 6014
33.3%
1 196
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11832
65.6%
. 6014
33.3%
1 196
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11832
65.6%
. 6014
33.3%
1 196
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11832
65.6%
. 6014
33.3%
1 196
 
1.1%

seats_9
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5945 
1.0
 
69

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5945
98.9%
1.0 69
 
1.1%

Length

2024-11-27T19:14:08.191385image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:14:08.433196image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5945
98.9%
1.0 69
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 11959
66.3%
. 6014
33.3%
1 69
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11959
66.3%
. 6014
33.3%
1 69
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11959
66.3%
. 6014
33.3%
1 69
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11959
66.3%
. 6014
33.3%
1 69
 
0.4%

seats_10
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
5996 
1.0
 
18

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5996
99.7%
1.0 18
 
0.3%

Length

2024-11-27T19:14:08.664333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:14:08.918745image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5996
99.7%
1.0 18
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 12010
66.6%
. 6014
33.3%
1 18
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12010
66.6%
. 6014
33.3%
1 18
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12010
66.6%
. 6014
33.3%
1 18
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12010
66.6%
. 6014
33.3%
1 18
 
0.1%

seats_14
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size352.5 KiB
0.0
6013 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18042
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 6013
> 99.9%
1.0 1
 
< 0.1%

Length

2024-11-27T19:14:09.151825image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-27T19:14:09.363120image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 6013
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 12027
66.7%
. 6014
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12027
66.7%
. 6014
33.3%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12027
66.7%
. 6014
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12027
66.7%
. 6014
33.3%
1 1
 
< 0.1%

Interactions

2024-11-27T19:13:35.626469image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:27.129511image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:28.745567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:30.723141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:32.703739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:34.105194image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:35.879291image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:27.424330image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:29.132292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:31.126603image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:32.947045image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:34.375469image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:36.140102image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:27.667409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:29.443278image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:31.475220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:33.192393image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:34.618293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:36.403211image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:27.888468image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:29.731706image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:31.811689image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:33.409313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:34.858013image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:36.628560image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:28.111253image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:30.027618image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:32.115622image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:33.612439image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:35.111906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:37.645483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:28.363400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:30.332781image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:32.469606image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:33.852819image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-27T19:13:35.362107image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-27T19:14:09.667803image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
enginefuel_Dieselfuel_LPGfuel_Petrolkm_drivenmax_powermileagename_Audiname_BMWname_Chevroletname_Daewooname_Datsunname_Fiatname_Forcename_Fordname_Hondaname_Hyundainame_Isuzuname_Jaguarname_Jeepname_Kianame_Landname_Lexusname_MGname_Mahindraname_Marutiname_Mercedes-Benzname_Mitsubishiname_Nissanname_Peugeotname_Renaultname_Skodaname_Tataname_Toyotaname_Volkswagenname_Volvoowner_Fourth & Above Ownerowner_Second Ownerowner_Test Drive Carowner_Third Ownerseats_10seats_14seats_4seats_5seats_6seats_7seats_8seats_9seller_type_Individualseller_type_Trustmark Dealerselling_pricetransmission_Manualyear
engine1.0000.7740.0740.7490.3050.717-0.4270.2740.3600.1660.0600.1110.0810.0560.2130.1610.3530.1020.1290.3000.0000.0590.0290.0000.5790.3950.2480.1140.0770.0000.1420.1970.1850.4240.1860.2020.0000.0800.1020.0650.1550.0610.3700.7180.1110.5940.3770.3030.1370.0000.4680.400-0.037
fuel_Diesel0.7741.0000.0790.9720.0660.2580.3350.0380.0660.0000.0110.0980.0360.0110.0960.0930.1760.0110.0260.0240.0020.0020.0000.0110.2830.2330.0350.0260.0000.0000.0190.0430.1180.1420.0530.0150.0040.0370.0170.0000.0450.0000.1160.3110.0130.3060.1140.0960.0220.0510.1820.0070.202
fuel_LPG0.0740.0791.0000.0640.0000.0600.0610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0220.0000.0000.0000.0000.0000.0000.0000.0180.0450.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0070.0000.0000.0000.0000.0000.0340.0000.0230.0000.0000.0000.0000.0000.0140.115
fuel_Petrol0.7490.9720.0641.0000.0710.2430.3560.0360.0630.0000.0120.1010.0340.0100.0890.1000.1740.0100.0250.0220.0000.0000.0000.0120.2740.2070.0330.0250.0000.0000.0250.0400.1100.1350.0490.0140.0000.0410.0180.0000.0440.0000.1150.3000.0090.2970.1110.0930.0130.0530.1750.0190.199
km_driven0.3050.0660.0000.0711.0000.045-0.1970.0000.0000.0000.0000.0000.0000.0000.0000.0000.0390.0000.0000.0000.0000.0000.0000.0000.0340.0440.0000.0370.0000.0000.0000.0000.0000.1780.0000.0000.0500.0290.0000.0280.0090.0000.0000.1140.0000.0900.0790.0000.0220.000-0.2940.016-0.573
max_power0.7170.2580.0600.2430.0451.000-0.3070.3750.4140.1260.0000.0990.0210.0180.0640.2010.1600.0730.2430.2750.0000.0270.2750.0630.1580.3540.4870.0940.0460.0000.0650.0270.1030.2040.0550.3750.0440.0650.1030.0430.0430.0000.7830.3490.0250.3260.1580.0480.2160.0480.6120.5130.162
mileage-0.4270.3350.0610.356-0.197-0.3071.0000.0890.0170.0950.0000.0530.0540.0000.0400.1850.2090.0980.0500.0240.0000.1520.0000.0270.4030.2730.1610.0620.0510.0000.1420.0180.0200.2520.0610.3550.0700.1060.0370.1020.1110.0610.2160.6020.1150.4390.2900.1880.0550.0000.0250.2490.345
name_Audi0.2740.0380.0000.0360.0000.3750.0891.0000.0000.0000.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0160.0440.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.2230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0850.0000.4060.2320.000
name_BMW0.3600.0660.0000.0630.0000.4140.0170.0001.0000.0000.0000.0000.0000.0000.0080.0080.0340.0000.0000.0000.0000.0000.0000.0000.0220.0530.0000.0000.0000.0000.0000.0000.0190.0080.0000.0000.0000.0000.0000.0000.0000.0000.0750.0000.0000.0210.0000.0000.1190.0000.4800.2720.012
name_Chevrolet0.1660.0000.0000.0000.0000.1260.0950.0000.0001.0000.0000.0000.0000.0000.0380.0390.0830.0000.0000.0000.0000.0000.0000.0000.0590.1210.0000.0000.0060.0000.0260.0050.0540.0380.0230.0000.0280.0350.0000.0120.1890.0000.0000.0180.0000.0130.0000.0540.0330.0000.0280.0340.120
name_Daewoo0.0600.0110.0000.0120.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.249
name_Datsun0.1110.0980.0000.1010.0000.0990.0530.0000.0000.0000.0001.0000.0000.0000.0120.0120.0390.0000.0000.0000.0000.0000.0000.0000.0260.0590.0000.0000.0000.0000.0000.0000.0230.0120.0000.0000.0000.0420.0000.0100.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0210.081
name_Fiat0.0810.0360.0000.0340.0000.0210.0540.0000.0000.0000.0000.0001.0000.0000.0060.0060.0330.0000.0000.0000.0000.0000.0000.0000.0210.0510.0000.0000.0000.0000.0000.0000.0180.0060.0000.0000.0000.0100.0000.0000.0000.0000.0000.0370.0000.0260.0000.0000.0000.0000.0000.0160.048
name_Force0.0560.0110.0000.0100.0000.0180.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0340.0000.0000.0000.0000.0000.0000.000
name_Ford0.2130.0960.0000.0890.0000.0640.0400.0000.0080.0380.0000.0120.0060.0001.0000.0520.1080.0000.0000.0000.0000.0000.0000.0000.0780.1570.0090.0000.0160.0000.0370.0150.0710.0520.0340.0000.0000.0000.0000.0000.0000.0000.0240.0960.0110.0650.0390.0170.0630.0000.0270.0380.045
name_Honda0.1610.0930.0000.1000.0000.2010.1850.0000.0080.0390.0000.0120.0060.0000.0521.0000.1090.0000.0000.0000.0000.0000.0000.0000.0780.1580.0090.0000.0160.0000.0370.0160.0720.0520.0340.0000.0260.0140.0000.0000.0000.0000.0240.0820.0110.0480.0390.0180.0890.0470.0000.0450.063
name_Hyundai0.3530.1760.0220.1740.0390.1600.2090.0280.0340.0830.0000.0390.0330.0000.1080.1091.0000.0000.0000.0190.0000.0000.0000.0000.1590.3160.0350.0000.0440.0000.0800.0430.1460.1080.0740.0000.0160.0000.0000.0000.0170.0000.0570.2330.0380.1780.0840.0470.0350.0000.0530.0160.033
name_Isuzu0.1020.0110.0000.0100.0000.0730.0980.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0990.0480.000
name_Jaguar0.1290.0260.0000.0250.0000.2430.0500.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0380.0000.2820.1110.000
name_Jeep0.3000.0240.0000.0220.0000.2750.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.0001.0000.0000.0000.0000.0000.0080.0340.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0230.0000.0140.0000.0000.0000.0000.2340.0000.089
name_Kia0.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0310.007
name_Land0.0590.0020.0000.0000.0000.0270.1520.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0260.0000.2630.0590.000
name_Lexus0.0290.0000.0000.0000.0000.2750.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.2550.0140.000
name_MG0.0000.0110.0000.0120.0000.0630.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0870.0590.007
name_Mahindra0.5790.2830.0180.2740.0340.1580.4030.0160.0220.0590.0000.0260.0210.0000.0780.0780.1590.0000.0000.0080.0000.0000.0000.0001.0000.2300.0230.0000.0300.0000.0570.0290.1060.0780.0530.0000.0000.0000.0000.0000.0050.0000.0400.5830.2500.4670.1800.2510.0580.0120.0890.0380.074
name_Maruti0.3950.2330.0450.2070.0440.3540.2730.0440.0530.1210.0000.0590.0510.0000.1570.1580.3160.0000.0150.0340.0000.0000.0000.0000.2301.0000.0540.0150.0670.0000.1170.0650.2120.1570.1090.0150.0210.0000.0000.0240.0310.0000.1160.1460.0500.1370.0670.0700.0450.0000.1740.0890.100
name_Mercedes-Benz0.2480.0350.0000.0330.0000.4870.1610.0000.0000.0000.0000.0000.0000.0000.0090.0090.0350.0000.0000.0000.0000.0000.0000.0000.0230.0541.0000.0000.0000.0000.0000.0000.0200.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.0000.0150.0000.0000.0890.0000.3660.2650.000
name_Mitsubishi0.1140.0260.0000.0250.0370.0940.0620.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0290.0000.0000.0000.0000.0380.0000.150
name_Nissan0.0770.0000.0000.0000.0000.0460.0510.0000.0000.0060.0000.0000.0000.0000.0160.0160.0440.0000.0000.0000.0000.0000.0000.0000.0300.0670.0000.0001.0000.0000.0030.0000.0270.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0000.0360.0060.0000.0000.0000.0000.0000.037
name_Peugeot0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.264
name_Renault0.1420.0190.0000.0250.0000.0650.1420.0000.0000.0260.0000.0000.0000.0000.0370.0370.0800.0000.0000.0000.0000.0000.0000.0000.0570.1170.0000.0000.0030.0001.0000.0000.0520.0370.0220.0000.0200.0320.0000.0270.0000.0000.0130.0480.0000.0380.0000.0060.0000.0000.0050.0150.097
name_Skoda0.1970.0430.0000.0400.0000.0270.0180.0000.0000.0050.0000.0000.0000.0000.0150.0160.0430.0000.0000.0000.0000.0000.0000.0000.0290.0650.0000.0000.0000.0000.0001.0000.0260.0150.0000.0000.0000.0200.0000.0000.0000.0000.0000.0480.0000.0360.0050.0000.0190.0000.0000.0540.026
name_Tata0.1850.1180.0150.1100.0000.1030.0200.0130.0190.0540.0000.0230.0180.0000.0710.0720.1460.0000.0000.0030.0000.0000.0000.0000.1060.2120.0200.0000.0270.0000.0520.0261.0000.0720.0480.0000.0000.0000.0000.0000.0280.0130.0280.0000.0220.0340.0510.0080.0590.0090.0340.0670.109
name_Toyota0.4240.1420.0000.1350.1780.2040.2520.0000.0080.0380.0000.0120.0060.0000.0520.0520.1080.0000.0000.0000.0000.0000.0000.0000.0780.1570.0090.0000.0160.0000.0370.0150.0721.0000.0340.0000.0000.0310.0000.0000.0000.0000.0240.2370.0110.1670.2660.0180.0320.0350.2180.0350.056
name_Volkswagen0.1860.0530.0000.0490.0000.0550.0610.0000.0000.0230.0000.0000.0000.0000.0340.0340.0740.0000.0000.0000.0000.0000.0000.0000.0530.1090.0000.0000.0000.0000.0220.0000.0480.0341.0000.0000.0000.0000.0000.0000.0000.0000.0110.0820.0000.0620.0240.0000.0220.0000.0000.0090.056
name_Volvo0.2020.0150.0000.0140.0000.3750.3550.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0220.0000.4060.1110.000
owner_Fourth & Above Owner0.0000.0040.0000.0000.0500.0440.0700.0000.0000.0280.0000.0000.0000.0000.0000.0260.0160.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0200.0000.0000.0000.0000.0001.0000.0950.0000.0400.0000.0000.0350.0000.0000.0130.0200.0000.0490.0000.0210.0220.223
owner_Second Owner0.0800.0370.0070.0410.0290.0650.1060.0000.0000.0350.0000.0420.0100.0000.0000.0140.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0320.0200.0000.0310.0000.0000.0951.0000.0000.1780.0000.0000.0270.0000.0000.0000.0150.0110.1120.0160.0740.0480.303
owner_Test Drive Car0.1020.0170.0000.0180.0000.1030.0370.2230.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0640.0000.7180.0720.023
owner_Third Owner0.0650.0000.0000.0000.0280.0430.1020.0000.0000.0120.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0160.0270.0000.0000.0000.0000.0000.0400.1780.0001.0000.0000.0000.0160.0000.0180.0150.0240.0000.0890.0040.0550.0480.272
seats_100.1550.0450.0000.0440.0090.0430.1110.0000.0000.1890.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0050.0310.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.1020.0000.0120.0000.0000.0040.0000.0000.0000.036
seats_140.0610.0000.0000.0000.0000.0000.0610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
seats_40.3700.1160.0000.1150.0000.7830.2160.0000.0750.0000.0000.0000.0000.0000.0240.0240.0570.0000.0000.0000.0000.0000.0000.0000.0400.1160.0000.0000.0000.0000.0130.0000.0280.0240.0110.0040.0350.0270.0000.0160.0000.0001.0000.2480.0000.0470.0150.0000.0000.0000.1410.0000.347
seats_50.7180.3110.0340.3000.1140.3490.6020.0000.0000.0180.0000.0000.0370.0230.0960.0820.2330.0000.0020.0230.0000.0000.0000.0000.5830.1460.0290.0160.0490.0000.0480.0480.0000.2370.0820.0000.0000.0000.0000.0000.1020.0000.2481.0000.1740.7740.3570.2080.0380.0200.1870.0000.108
seats_60.1110.0130.0000.0090.0000.0250.1150.0000.0000.0000.0000.0000.0000.0000.0110.0110.0380.0000.0000.0000.0000.0000.0000.0000.2500.0500.0000.0000.0000.0000.0000.0000.0220.0110.0000.0000.0000.0000.0000.0180.0000.0000.0000.1741.0000.0310.0000.0000.0170.0000.0000.0210.077
seats_70.5940.3060.0230.2970.0900.3260.4390.0000.0210.0130.0000.0200.0260.0340.0650.0480.1780.0120.0000.0140.0000.0000.0000.0000.4670.1370.0150.0290.0360.0000.0380.0360.0340.1670.0620.0000.0130.0000.0000.0150.0120.0000.0470.7740.0311.0000.0700.0380.0000.0060.2250.0090.131
seats_80.3770.1140.0000.1110.0790.1580.2900.0000.0000.0000.0000.0000.0000.0000.0390.0390.0840.0000.0000.0000.0000.0000.0000.0000.1800.0670.0000.0000.0060.0000.0000.0050.0510.2660.0240.0000.0200.0150.0000.0240.0000.0000.0150.3570.0000.0701.0000.0080.0120.0000.0300.0050.070
seats_90.3030.0960.0000.0930.0000.0480.1880.0000.0000.0540.0000.0000.0000.0000.0170.0180.0470.0000.0000.0000.0000.0000.0000.0000.2510.0700.0000.0000.0000.0000.0060.0000.0080.0180.0000.0000.0000.0110.0000.0000.0000.0000.0000.2080.0000.0380.0081.0000.0260.0000.0000.0270.046
seller_type_Individual0.1370.0220.0000.0130.0220.2160.0550.0850.1190.0330.0000.0000.0000.0000.0630.0890.0350.0000.0380.0000.0000.0260.0110.0520.0580.0450.0890.0000.0000.0000.0000.0190.0590.0320.0220.0220.0490.1120.0640.0890.0040.0000.0000.0380.0170.0000.0120.0261.0000.1860.2250.2160.147
seller_type_Trustmark Dealer0.0000.0510.0000.0530.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0470.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.0090.0350.0000.0000.0000.0160.0000.0040.0000.0000.0000.0200.0000.0060.0000.0000.1861.0000.0000.0480.041
selling_price0.4680.1820.0000.175-0.2940.6120.0250.4060.4800.0280.0000.0000.0000.0000.0270.0000.0530.0990.2820.2340.0490.2630.2550.0870.0890.1740.3660.0380.0000.0000.0050.0000.0340.2180.0000.4060.0210.0740.7180.0550.0000.0000.1410.1870.0000.2250.0300.0000.2250.0001.0000.4650.705
transmission_Manual0.4000.0070.0140.0190.0160.5130.2490.2320.2720.0340.0000.0210.0160.0000.0380.0450.0160.0480.1110.0000.0310.0590.0140.0590.0380.0890.2650.0000.0000.0000.0150.0540.0670.0350.0090.1110.0220.0480.0720.0480.0000.0000.0000.0000.0210.0090.0050.0270.2160.0480.4651.0000.153
year-0.0370.2020.1150.199-0.5730.1620.3450.0000.0120.1200.2490.0810.0480.0000.0450.0630.0330.0000.0000.0890.0070.0000.0000.0070.0740.1000.0000.1500.0370.2640.0970.0260.1090.0560.0560.0000.2230.3030.0230.2720.0360.0000.3470.1080.0770.1310.0700.0460.1470.0410.7050.1531.000

Missing values

2024-11-27T19:13:38.184820image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-27T19:13:38.869761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

yearselling_pricekm_drivenmileageenginemax_powername_Audiname_BMWname_Chevroletname_Daewooname_Datsunname_Fiatname_Forcename_Fordname_Hondaname_Hyundainame_Isuzuname_Jaguarname_Jeepname_Kianame_Landname_Lexusname_MGname_Mahindraname_Marutiname_Mercedes-Benzname_Mitsubishiname_Nissanname_Peugeotname_Renaultname_Skodaname_Tataname_Toyotaname_Volkswagenname_Volvofuel_Dieselfuel_LPGfuel_Petrolseller_type_Individualseller_type_Trustmark Dealertransmission_Manualowner_Fourth & Above Ownerowner_Second Ownerowner_Test Drive Carowner_Third Ownerseats_4seats_5seats_6seats_7seats_8seats_9seats_10seats_14
0201445000014550023.40124874.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.00.00.00.01.00.00.00.00.00.00.0
1201437000012000021.141498103.520.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01.00.00.01.00.01.00.01.00.00.00.01.00.00.00.00.00.00.0
2201022500012700023.00139690.000.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.00.00.00.01.00.00.00.00.00.00.0
3200713000012000016.10129888.200.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.01.00.00.00.00.00.01.00.00.00.00.00.00.0
420174400004500020.14119781.860.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.01.00.00.00.00.00.01.00.00.00.00.00.00.0
520079600017500017.30106157.500.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.01.00.00.00.00.00.01.00.00.00.00.00.00.0
6200145000500016.1079637.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.01.00.01.00.00.01.00.00.00.00.00.00.00.0
720113500009000023.59136467.100.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.00.01.00.01.00.00.00.00.00.01.00.00.00.00.00.00.0
8201320000016900020.00139968.100.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.00.00.00.01.00.00.00.00.00.00.0
920145000006800019.011461108.450.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.01.00.00.01.00.01.00.01.00.00.00.01.00.00.00.00.00.00.0
yearselling_pricekm_drivenmileageenginemax_powername_Audiname_BMWname_Chevroletname_Daewooname_Datsunname_Fiatname_Forcename_Fordname_Hondaname_Hyundainame_Isuzuname_Jaguarname_Jeepname_Kianame_Landname_Lexusname_MGname_Mahindraname_Marutiname_Mercedes-Benzname_Mitsubishiname_Nissanname_Peugeotname_Renaultname_Skodaname_Tataname_Toyotaname_Volkswagenname_Volvofuel_Dieselfuel_LPGfuel_Petrolseller_type_Individualseller_type_Trustmark Dealertransmission_Manualowner_Fourth & Above Ownerowner_Second Ownerowner_Test Drive Carowner_Third Ownerseats_4seats_5seats_6seats_7seats_8seats_9seats_10seats_14
600420112000007300019.7079646.300.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.01.00.00.00.00.00.01.00.00.00.00.00.00.0
600519974000012000016.1079637.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.01.00.00.00.00.01.00.00.00.00.00.00.00.0
600620173400004500023.9599867.100.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.01.00.00.00.00.00.01.00.00.00.00.00.00.0
600720133800002500018.50119782.850.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.01.00.00.00.00.00.01.00.00.00.00.00.00.0
600820173600008000020.5199867.040.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.01.00.00.00.00.00.01.00.00.00.00.00.00.0
6009200812000019100017.92108662.100.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.01.00.00.00.00.00.01.00.00.00.00.00.00.0
601020132600005000018.9099867.100.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.01.00.01.00.00.00.01.00.00.00.00.00.00.0
6011201332000011000018.50119782.850.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.01.00.01.00.00.00.00.00.01.00.00.00.00.00.00.0
6012200713500011900016.801493110.000.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.01.01.00.00.00.00.01.00.00.00.00.00.00.0
6013200938200012000019.30124873.900.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.00.00.00.01.00.00.00.00.00.00.0

Duplicate rows

Most frequently occurring

yearselling_pricekm_drivenmileageenginemax_powername_Audiname_BMWname_Chevroletname_Daewooname_Datsunname_Fiatname_Forcename_Fordname_Hondaname_Hyundainame_Isuzuname_Jaguarname_Jeepname_Kianame_Landname_Lexusname_MGname_Mahindraname_Marutiname_Mercedes-Benzname_Mitsubishiname_Nissanname_Peugeotname_Renaultname_Skodaname_Tataname_Toyotaname_Volkswagenname_Volvofuel_Dieselfuel_LPGfuel_Petrolseller_type_Individualseller_type_Trustmark Dealertransmission_Manualowner_Fourth & Above Ownerowner_Second Ownerowner_Test Drive Carowner_Third Ownerseats_4seats_5seats_6seats_7seats_8seats_9seats_10seats_14# duplicates
0200820000016300017.80139968.000.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.00.01.00.01.00.00.00.00.00.01.00.00.00.00.00.00.02
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